Unverified50% confidenceFactExact time
RASO's contributor selection selects contributors based on prediction dependency and participation in local optimization.
1
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50%
Confidence
Long-term
Relevance
11/2/2026
First Seen
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UnverifiedRASO (Role-Aware Server Optimization) is a multimodal federated learning method for missing-modality scenarios that improves model performance by coordinating contributor selection with role-specific server updates.71% similarUnverifiedExperimental results show that by coordinating contributor selection with role-specific server updates, RASO can significantly improve model performance under missing-modality conditions.70% similarUnverifiedThe core contribution of this method lies in extending sample selection from point-wise to region-wise and incorporating uncertainty measurement, providing a new idea for class-aware active domain adaptation.62% similarUnverified使用高能力模型进行优化的推荐交互模式包括：明确定义成功指标、提供基线方案、让模型提出改进假设并预估收益、将执行结果反馈给模型迭代调整62% similarUnverifiedWhen choosing an annotation tool, prioritize two things: whether it supports importing model prediction results and whether it is convenient for manual correction.60% similar
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